Evolutionary algorithm-based path planning with multiple quadrotors using ultra-wideband for warehouse inventory management

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ID: 286933
2023
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Abstract
Quadrotors play a crucial role in integrating automation into warehouses, increasing overall efficiency in collecting information on products stored at high altitudes. Studies investigating quadrotor use in warehouses mostly rely on simultaneous localization and mapping for localization. These studies only employ a single quadrotor, which may not be sufficient for large-scale warehouses due to their limited endurance. This study aims to implement a path planning algorithm based on an evolutionary algorithm to automate inventory management with multiple quadrotors. Ultra-wideband localization was used to localize the quadrotor and store location information of products in a warehouse. The energy consumption model of the Crazyflie quadrotor was obtained by performing certain maneuvers and extracting the recovered energy through charging. Results show that the energy consumption model allowed the evolutionary algorithm-based path planning algorithm to minimize the quadrotor energy consumption and reach the product at a desired yaw. A hybrid algorithm combining the features of two state-of-the-art PSO algorithms achieved better performance against other variants in terms of energy consumption and robustness against violation in constraints while having a negligible effect on computational time. In the presence of multiple quadrotors, the algorithm was able to select the appropriate quadrotor for a single product and was effective in assigning these quadrotors to multiple products. Simulations show that the quadrotor was able to iii perform the trajectory accurately with only a maximum energy consumption difference of 17.21 J. Actual experiments further proved the accuracy of the energy consumption model, with a maximum energy consumption difference of 32.15 J in the path planning of multiple quadrotors.
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Authors De Guzman, Carlos James P.
Journal Malay Journal
Year 2023
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_mecheng/18
Keywords Keywords not found

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